UNASSIGNED: Mobile health (mHealth) interventions have immense potential to support disease self-management for people with complex medical conditions following treatment regimens that involve taking medicine and other self-management activities. However, there is no consensus on what discrete behavior change techniques (BCTs) should be used in an effective adherence and self-management-promoting mHealth solution for any chronic illness. Reviewing the extant literature to identify effective, cross-cutting BCTs in mHealth interventions for adherence and self-management promotion could help accelerate the development, evaluation, and dissemination of behavior change interventions with potential generalizability across complex medical conditions.
UNASSIGNED: This study aimed to identify cross-cutting, mHealth-based BCTs to incorporate into effective mHealth adherence and self-management interventions for people with complex medical conditions, by systematically reviewing the literature across chronic medical conditions with similar adherence and self-management demands.
UNASSIGNED: A registered systematic review was conducted to identify published evaluations of mHealth adherence and self-management interventions for chronic medical conditions with complex adherence and self-management demands. The methodological characteristics and BCTs in each study were extracted using a standard data collection form.
UNASSIGNED: A total of 122 studies were reviewed; the majority involved people with type 2 diabetes (28/122, 23%), asthma (27/122, 22%), and type 1 diabetes (19/122, 16%). mHealth interventions rated as having a positive outcome on adherence and self-management used more BCTs (mean 4.95, SD 2.56) than interventions with no impact on outcomes (mean 3.57, SD 1.95) or those that used >1 outcome measure or analytic approach (mean 3.90, SD 1.93; P=.02). The following BCTs were associated with positive outcomes: self-monitoring outcomes of behavior (39/59, 66%), feedback on outcomes of behavior (34/59, 58%), self-monitoring of behavior (34/59, 58%), feedback on behavior (29/59, 49%), credible source (24/59, 41%), and goal setting (behavior; 14/59, 24%). In adult-only samples, prompts and cues were associated with positive outcomes (34/45, 76%). In adolescent and young adult samples, information about health consequences (1/4, 25%), problem-solving (1/4, 25%), and material reward (behavior; 2/4, 50%) were associated with positive outcomes. In interventions explicitly targeting medicine taking, prompts and cues (25/33, 76%) and credible source (13/33, 39%) were associated with positive outcomes. In interventions focused on self-management and other adherence targets, instruction on how to perform the behavior (8/26, 31%), goal setting (behavior; 8/26, 31%), and action planning (5/26, 19%) were associated with positive outcomes.
UNASSIGNED: To support adherence and self-management in people with complex medical conditions, mHealth tools should purposefully incorporate effective and developmentally appropriate BCTs. A cross-cutting approach to BCT selection could accelerate the development of much-needed mHealth interventions for target populations, although mHealth intervention developers should continue to consider the unique needs of the target population when designing these tools.

BACKGROUND: The use of mobile devices for delivering health-related services (mobile health [mHealth]) has rapidly increased, leading to a demand for summarizing the state of the art and practice through systematic reviews. However, the systematic review process is a resource-intensive and time-consuming process. Generative artificial intelligence (AI) has emerged as a potential solution to automate tedious tasks.
OBJECTIVE: This study aimed to explore the feasibility of using generative AI tools to automate time-consuming and resource-intensive tasks in a systematic review process and assess the scope and limitations of using such tools.
METHODS: We used the design science research methodology. The solution proposed is to use cocreation with a generative AI, such as ChatGPT, to produce software code that automates the process of conducting systematic reviews.
RESULTS: A triggering prompt was generated, and assistance from the generative AI was used to guide the steps toward developing, executing, and debugging a Python script. Errors in code were solved through conversational exchange with ChatGPT, and a tentative script was created. The code pulled the mHealth solutions from the Google Play Store and searched their descriptions for keywords that hinted toward evidence base. The results were exported to a CSV file, which was compared to the initial outputs of other similar systematic review processes.
CONCLUSIONS: This study demonstrates the potential of using generative AI to automate the time-consuming process of conducting systematic reviews of mHealth apps. This approach could be particularly useful for researchers with limited coding skills. However, the study has limitations related to the design science research methodology, subjectivity bias, and the quality of the search results used to train the language model.

BACKGROUND: Successful post-knee replacement rehabilitation requires adequate access to health information, social support, and periodic monitoring by a health professional. Mobile health (mHealth) and computer-based technologies are used for rehabilitation and remote monitoring. The extent of technology use and its function in post-knee replacement rehabilitation care in low and middle-income settings are unknown.
OBJECTIVE: To inform future mHealth intervention development, we conducted a scoping review to map the features and functionality of existing technologies and determine users\' perspectives on telerehabilitation and technology for self-management.
METHODS: We followed the Joanna Briggs Institute methodology for scoping reviews. We searched the Embase, Medline, PsycINFO via OVID, and Cochrane Central Register of Controlled Trials databases for manuscripts published from 2001 onward. We included original research articles reporting the use of mobile or computer-based technologies by patients, health care providers, researchers, or family members. Studies were divided into the following 3 categories based on the purpose: validation studies, clinical evaluation, and end user feedback. We extracted general information on study design, technology features, proposed function, and perspectives of health care providers and patients. The protocol for this review is accessible in the Open Science Framework.
RESULTS: Of the 5960 articles, 158 that reported from high-income settings contributed to the qualitative summary (64 studies on mHealth or telerehabilitation programs, 28 validation studies, 38 studies describing users\' perceptions). The highest numbers of studies were from Europe or the United Kingdom and North America regarding the use of a mobile app with or without wearables and reported mainly in the last decade. No studies were from low and middle-income settings. The primary functions of technology for remote rehabilitation were education to aid recovery and enable regular, appropriate exercises; monitoring progress of pain (n=19), activity (n=20), and exercise adherence (n=30); 1 or 2-way communication with health care professionals to facilitate the continuum of care (n=51); and goal setting (n=23). Assessment of range of motion (n=16) and gait analysis (n=10) were the commonly validated technologies developed to incorporate into a future rehabilitation program. Few studies (n=14) reported end user involvement during the development stage. We summarized the reasons for satisfaction and dissatisfaction among users across various technologies.
CONCLUSIONS: Several existing mobile and computer-based technologies facilitate post-knee replacement rehabilitation care for patients and health care providers. However, they are limited to high-income settings and may not be extrapolated to low-income settings. A systematic needs assessment of patients undergoing knee replacement and health care providers involved in rehabilitation, involving end users at all stages of development and evaluation, with clear reporting of the development and clinical evaluation can make post-knee replacement rehabilitation care in resource-poor settings accessible and cost-effective.

BACKGROUND: The prevention of oral health diseases is a key public health issue and a major challenge for racial and ethnic minority groups, who often face barriers in accessing dental care. Daily toothbrushing is an important self-care behavior necessary for sustaining good oral health, yet engagement in regular brushing remains a challenge. Identifying strategies to promote engagement in regular oral self-care behaviors among populations at risk of poor oral health is critical.
OBJECTIVE: The formative research described here focused on creating messages for a digital oral self-care intervention targeting a racially and ethnically diverse population. Theoretically grounded strategies (reciprocity, reciprocity-by-proxy, and curiosity) were used to promote engagement in 3 aspects: oral self-care behaviors, an oral care smartphone app, and digital messages. A web-based participatory co-design approach was used to develop messages that are resource efficient, appealing, and novel; this approach involved dental experts, individuals from the general population, and individuals from the target population-dental patients from predominantly low-income racial and ethnic minority groups. Given that many individuals from racially and ethnically diverse populations face anonymity and confidentiality concerns when participating in research, we used an approach to message development that aimed to mitigate these concerns.
METHODS: Messages were initially developed with feedback from dental experts and Amazon Mechanical Turk workers. Dental patients were then recruited for 2 facilitator-mediated group webinar sessions held over Zoom (Zoom Video Communications; session 1: n=13; session 2: n=7), in which they provided both quantitative ratings and qualitative feedback on the messages. Participants interacted with the facilitator through Zoom polls and a chat window that was anonymous to other participants. Participants did not directly interact with each other, and the facilitator mediated sessions by verbally asking for message feedback and sharing key suggestions with the group for additional feedback. This approach plausibly enhanced participant anonymity and confidentiality during the sessions.
RESULTS: Participants rated messages highly in terms of liking (overall rating: mean 2.63, SD 0.58; reciprocity: mean 2.65, SD 0.52; reciprocity-by-proxy: mean 2.58, SD 0.53; curiosity involving interactive oral health questions and answers: mean 2.45, SD 0.69; curiosity involving tailored brushing feedback: mean 2.77, SD 0.48) on a scale ranging from 1 (do not like it) to 3 (like it). Qualitative feedback indicated that the participants preferred messages that were straightforward, enthusiastic, conversational, relatable, and authentic.
CONCLUSIONS: This formative research has the potential to guide the design of messages for future digital health behavioral interventions targeting individuals from diverse racial and ethnic populations. Insights emphasize the importance of identifying key stimuli and tasks that require engagement, gathering multiple perspectives during message development, and using new approaches for collecting both quantitative and qualitative data while mitigating anonymity and confidentiality concerns.

UNASSIGNED: Good physical and mental health are essential for healthy ageing. Holistic mobile health (mHealth) interventions-including at least three components: physical activity, diet, and mental health-could support both physical and mental health and be scaled to the population level. This review aims to describe the characteristics of holistic mHealth interventions and their effects on related behavioural and health outcomes among adults from the general population.
UNASSIGNED: In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, Scopus, China National Knowledge Infrastructure, and Google Scholar (first 200 records). The initial search covered January 1, 2011, to April 13, 2022, and an updated search extended from April 13, 2022 to August 30, 2023. Randomised controlled trials (RCTs) and non-randomised studies of interventions (NRSIs) were included if they (i) were delivered via mHealth technologies, (ii) included content on physical activity, diet, and mental health, and (iii) targeted adults (≥18 years old) from the general population or those at risk of non-communicable diseases (NCDs) or mental disorders. Studies were excluded if they targeted pregnant women (due to distinct physiological responses), individuals with pre-existing NCDs or mental disorders (to emphasise prevention), or primarily utilised web, email, or structured phone support (to focus on mobile technologies without exclusive human support). Data (summary data from published reports) extraction and risk-of-bias assessment were completed by two reviewers using a standard template and Cochrane risk-of-bias tools, respectively. Narrative syntheses were conducted for all studies, and random-effects models were used in the meta-analyses to estimate the pooled effect of interventions for outcomes with comparable data in the RCTs. The study was registered in PROSPERO, CRD42022315166.
UNASSIGNED: After screening 5488 identified records, 34 studies (25 RCTs and 9 pre-post NRSIs) reported in 43 articles with 5691 participants (mean age 39 years, SD 12.5) were included. Most (91.2%, n = 31/34) were conducted in high-income countries. The median intervention duration was 3 months, and only 23.5% (n = 8/34) of studies reported follow-up data. Mobile applications, short-message services, and mobile device-compatible websites were the most common mHealth delivery modes; 47.1% (n = 16/34) studies used multiple mHealth delivery modes. Of 15 studies reporting on weight change, 9 showed significant reductions (6 targeted on individuals with overweight or obesity), and in 10 studies reporting perceived stress levels, 4 found significant reductions (all targeted on general adults). In the meta-analysis, holistic mHealth interventions were associated with significant weight loss (9 RCTs; mean difference -1.70 kg, 95% CI -2.45 to -0.95; I2 = 89.00%) and a significant reduction in perceived stress levels (6 RCTs; standardised mean difference [SMD] -0.32; 95% CI -0.52 to -0.12; I2 = 14.52%). There were no significant intervention effects on self-reported moderate-to-vigorous physical activity (5 RCTs; SMD 0.21; 95%CI -0.25 to 0.67; I2 = 74.28%) or diet quality scores (5 RCTs; SMD 0.21; 95%CI -0.47 to 0.65; I2 = 62.27%). All NRSIs were labelled as having a serious risk of bias overall; 56% (n = 14/25) of RCTs were classified as having some concerns, and the others as having a high risk of bias.
UNASSIGNED: Findings from identified studies suggest that holistic mHealth interventions may aid reductions in weight and in perceived stress levels, with small to medium effect sizes. The observed effects on diet quality scores and self-reported moderate-to-vigorous physical activity were less clear and require more research. High-quality RCTs with longer follow-up durations are needed to provide more robust evidence. To promote population health, future research should focus on vulnerable populations and those in middle- and low-income countries. Optimal combinations of delivery modes and components to improve efficacy and sustain long-term effects should also be explored.
UNASSIGNED: National Research Foundation, Prime Minister\'s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme and Physical Activity and Nutrition Determinants in Asia (PANDA) Research Programme.

Smartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers\' availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched.
This study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert.
We developed and compared 4 analytical models to predict VFRs\' response behaviors based on emergency event characteristics, volunteers\' demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder\'s previous behavior.
The highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs\' demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs\' previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy.
VFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.

COVID-19 remains an ongoing public health crisis. Black Americans remain underrepresented among those vaccinated and overrepresented in both COVID-19 morbidity and mortality. Medical misinformation, specifically related to COVID-19, has exacerbated the impact of the disease in Black American communities. Communication tools and strategies to build relationships and disseminate credible and trustworthy diagnostic and preventative health information are necessary to improve outcomes and equity for historically oppressed populations.
As the initial phase of a larger mixed methods project to develop, pilot, and evaluate a mobile health (mHealth) intervention among a population at high risk for COVID-19 and cardiovascular comorbidities, this study sought to explore COVID-19 information behavior among Black Americans. Specifically, this study examined (1) preferences for COVID-19 education via mHealth, (2) barriers and facilitators to COVID-19 education and diagnostic testing and routine care for associated cardiovascular and respiratory comorbidities in the local community, and (3) key content for inclusion in a COVID-19 mHealth app.
This qualitative study used principles of community-based participatory research and information systems research to conduct 7 focus groups across 3 sites. Focus groups were audio recorded and transcribed for thematic analysis using an abductive approach.
The study sample included 54 individuals across sites with a mean age of 50.24 (SD 11.76; range 20-71) years. Participants were primarily female (n=42, 78%) and Black (n=54, 100%) with varied education levels. Over half (n=29, 54%) of the participants were employed full-time, and nearly three-fourths (n=40, 74%) had household incomes Increasing transparency and building trust are 2 key strategies that may improve the impact of health information messaging in Black communities. Focusing on content over context fails in the provision of critical health information and perpetuates health inequities by reinforcing systemic and structural racism. COVID-19 messaging must consider contextual information, patient needs and preferences, and patient information-seeking and information-search behaviors to establish trust and credibility, positively impact patient health outcomes, and improve health equity.

BACKGROUND: Mobile health (mHealth) interventions can deliver personalized behavioral support to users in daily contexts. These interventions have been increasingly adopted to support individuals who require low-cost and low-burden support. Prior research has demonstrated the feasibility and acceptability of an mHealth intervention app (CareQOL) designed for use with informal care partners. To further optimize the intervention delivery, we need to investigate how care partners, many of whom lack the time for self-care, react and act in response to different behavioral messages.
OBJECTIVE: The goal of this study was to understand the factors that impact care partners\' decision-making and actions in response to different behavioral messages. Insights from this study will help optimize future tailored and personalized behavioral interventions.
METHODS: We conducted semistructured interviews with participants who had recently completed a 3-month randomized controlled feasibility trial of the CareQOL mHealth intervention app. Of the 36 participants from the treatment group of the randomized controlled trial, 23 (64%) participated in these interviews. To prepare for each interview, the team first selected representative behavioral messages (eg, targeting different health dimensions) and presented them to participants during the interview to probe their influence on participants\' thoughts and actions. The time of delivery, self-reported perceptions of the day, and user ratings of a message were presented to the participants during the interviews to assist with recall.
RESULTS: The interview data showed that after receiving a message, participants took various actions in response to different messages. Participants performed suggested behaviors or adjusted them either immediately or in a delayed manner (eg, sometimes up to a month later). We identified 4 factors that shape the variations in user actions in response to different behavioral messages: uncertainties about the workload required to perform suggested behaviors, concerns about one\'s ability to routinize suggested behaviors, in-the-moment willingness and ability to plan for suggested behaviors, and overall capability to engage with the intervention.
CONCLUSIONS: Our study showed that care partners use mHealth behavioral messages differently regarding the immediacy of actions and the adaptation to suggested behaviors. Multiple factors influence people\'s perceptions and decisions regarding when and how to take actions. Future systems should consider these factors to tailor behavioral support for individuals and design system features to support the delay or adaptation of the suggested behaviors. The findings also suggest extending the assessment of user adherence by considering the variations in user actions on behavioral support (ie, performing suggested or adjusted behaviors immediately or in a delayed manner).
UNASSIGNED: RR2-10.2196/32842.

Percutaneous endoscopic gastrostomy (PEG) is commonly chosen for long-term enteral nutrition support. However, common complications of PEG include wound infection, leakage, obstruction, bleeding, dislodgement, pneumonia, peritonitis, and more. The anticipation of these complications by both patients and their family caregivers underscores the essential requirement of ongoing technical guidance for the daily care of PEG and the adoption of preventative strategies.
This study aimed to establish and compare a health education program utilizing a tracking system for PEG using a mobile app (PEG app) and instant messaging software versus a paper-based health education program with instant messaging software. Their effectiveness in preventing complications, avoiding hospital readmissions, improving self-care practices, and enhancing quality of life outcomes was assessed.
A randomized controlled trial design was used, and the study sample consisted of patients from a medical center in central Taiwan who underwent thoracic surgery or gastroenterology procedures. Inclusion criteria were being a new case undergoing his or her first gastric tube insertion and having the ability to operate a smartphone. Exclusion criteria were cases requiring tube replacement or nasogastric tubes. A total of 74 participants were enrolled, with 37 participants in the experimental group and 37 participants in the control group. Data collection took place from hospitalization until 1 month after discharge. The experimental group received care using the gastric tube tracking system (PEG app) and the Line app that included phone, text, and photo capture capabilities, while the control group received routine nursing care and used the Line app.
The experimental group demonstrated a significant reduction in the occurrence of complications compared with the control group (χ21=12.087, P=.001). Specifically, the occurrence of leakage events was significantly lower in the experimental group than in the control group (χ21=12.906, P=.001). However, the experimental group exhibited superior self-care ability compared with the control group (t72=2.203, P=.03). There was no significant difference in overall quality of life scores between the experimental and control groups (t72=1.603, P=.11). However, the experimental group showed better social aspects of quality of life than the control group (t72=2.164, P=.03).
Integration of the PEG app with instant messaging can enhance self-care ability, improve social aspects of quality of life, and reduce complications. The study results suggest that the PEG app could be used as an adjunct tool to promote patients\' self-directed management of their gastric tube at home, particularly for patients who have undergone their first PEG placement and are being discharged from the hospital.
Chinese Clinical Trial Registry ChiCTR2300071271; https://tinyurl.com/4vvy584e.

BACKGROUND: Chemsex-the use of psychoactive drugs to enhance the sexual experience-is an increasing phenomenon globally. Despite the increasing burden and associated harms of chemsex, evidence-based interventions (ie, behavioral and pharmacological) for chemsex users are nonexistent.
OBJECTIVE: In this study, we assessed the usability and acceptability of a mobile health (mHealth)-delivered safer chemsex package (\"PartyPack\") as a sexual harm reduction strategy among men who have sex with men in Malaysia-a setting where chemsex is becoming increasingly prevalent.
METHODS: This study is part of a larger smartphone app-based intervention (ie, JomPrEP; University of Connecticut) designed to improve access to HIV prevention services among Malaysian men who have sex with men. A total of 50 participants were recruited from the Greater Kuala Lumpur region of Malaysia to use the JomPrEP app, which included a feature allowing participants to order PartyPack, for 30 days (March-April 2022). The usability and acceptability of the PartyPack were assessed using self-report, app analytics, and exit interviews (n=20).
RESULTS: Overall, 8% (4/50) of participants reported having engaged in chemsex in the past 6 months; however, engagement in condomless sex (34/50, 68%) and group sex (9/50, 18%) was much higher. A total of 43 (86%) participants ordered PartyPack, of which 27 (63%) made multiple orders during the 30 days. Most participants (41/43, 95%) reported being satisfied with the PartyPack order feature in the app, with 91% (39/43) indicating the order and tracking process was easy. Thematic data exploration further revealed important information for understanding (eg, items included in the package, use of mHealth platform to order package, and discreetness of the PartyPack box and order and delivery) and refining the logistical preferences (eg, using branded items and allowing customization during order).
CONCLUSIONS: Our findings provide strong evidence of the usability and acceptability of a mHealth-delivered safer chemsex package as a potential sexual harm reduction tool among this underserved population. Replication in a study with a larger sample size to test the efficacy of the PartyPack is warranted.